SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 6748 papers

TitleStatusHype
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Coherent Probabilistic Aggregate Queries on Long-horizon ForecastsCode1
Transferable Time-Series Forecasting under Causal Conditional ShiftCode1
Unsupervised Change Detection of Extreme Events Using ML On-BoardCode1
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift EstimationCode1
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual DataCode1
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
Truth-Conditional Captions for Time Series DataCode1
Sig-Wasserstein GANs for Time Series GenerationCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Testing and Estimating Structural Breaks in Time Series and Panel Data in StataCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
ClaSP - Time Series SegmentationCode1
Neural Flows: Efficient Alternative to Neural ODEsCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
An Empirical Evaluation of Time-Series Feature SetsCode1
SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative ArcsCode1
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
On the difficulty of learning chaotic dynamics with RNNsCode1
Dynamical Wasserstein Barycenters for Time-series ModelingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified